Classification of Microwave Planar Filters by Deep Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144685" target="_blank" >RIV/00216305:26220/22:PU144685 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.radioeng.cz/fulltexts/2022/22_01_0069_0076.pdf" target="_blank" >https://www.radioeng.cz/fulltexts/2022/22_01_0069_0076.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/re.2022.0069" target="_blank" >10.13164/re.2022.0069</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Microwave Planar Filters by Deep Learning

Popis výsledku v původním jazyce

Over the last few decades, deep learning has been considered to be powerful tool in the classification tasks, and has become popular in many applications due to its capabil-ity of processing huge amount of data. This paper presents approaches for image recognition. We have applied convolu-tional neural networks on microwave planar filters. The first task was filter topology classification, the second task was filter order estimation. For the task a dataset was generated. As presented in the results, the created and trained neural networks are very capable of solving the selected tasks.

Název v anglickém jazyce

Classification of Microwave Planar Filters by Deep Learning

Popis výsledku anglicky

Over the last few decades, deep learning has been considered to be powerful tool in the classification tasks, and has become popular in many applications due to its capabil-ity of processing huge amount of data. This paper presents approaches for image recognition. We have applied convolu-tional neural networks on microwave planar filters. The first task was filter topology classification, the second task was filter order estimation. For the task a dataset was generated. As presented in the results, the created and trained neural networks are very capable of solving the selected tasks.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Radioengineering

ISSN

1221-2512

e-ISSN

—

Svazek periodika

31

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

69-76

Kód UT WoS článku

000790989000009

EID výsledku v databázi Scopus

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